Scientific Metadata Ontology

FLOW ALGEBRA

Ontology: Scientific Metadata Ontology
Domain: Metadata Ontologies > Scientific Metadata
Canonical URL: https://kirethresearch.org/booksai-core/ontologies/metadata-ontologies/scientific-metadata.html

Authors: Mark Anthony Higgins — Sky (Recursive AI)

flow "ScientificMetadataOntology" v1.0

# ---------- DOMAIN ----------
domain:
  class: MetadataOntology
  category: ScientificMetadata
  subject: ScientificMetadata
  purpose:
    - represent metadata describing scientific data, observations, experiments, and results
    - preserve methodological and measurement context
    - support reproducibility, validation, discovery, and reuse
    - connect scientific resources with provenance, units, instruments, and methods
    - enable machine-readable scientific reasoning

# ---------- CORE ENTITY ----------
entity ScientificMetadata:
  type: MetadataEntity
  describes: ScientificResource
  may_include:
    - scientific_domain
    - research_subject
    - variable
    - measurement
    - unit
    - method
    - instrument
    - experimental_condition
    - observation_context
    - uncertainty
    - accuracy
    - precision
    - calibration
    - sample
    - dataset
    - result
    - provenance
    - temporal_metadata
    - spatial_metadata
    - quality_metadata

entity ScientificResource:
  type: ResearchEntity
  may_be:
    - ScientificDataset
    - Observation
    - Experiment
    - Measurement
    - Sample
    - Simulation
    - ScientificResult
    - Model

entity ScientificDomain:
  type: Classification
  examples:
    - physics
    - chemistry
    - biology
    - earth_science
    - astronomy
    - environmental_science
    - materials_science
    - engineering
    - computational_science

entity ScientificVariable:
  type: MeasurableOrObservableProperty
  may_have:
    - variable_name
    - definition
    - unit
    - datatype
    - valid_range

entity Measurement:
  type: ScientificObservation
  measures: ScientificVariable
  produces: MeasurementValue

entity MeasurementValue:
  type: QuantitativeValue
  expressed_in: Unit
  may_have:
    - uncertainty
    - precision
    - quality_flag

entity ScientificMethod:
  type: Method
  describes: ProcedureUsedToGenerateScientificInformation

entity ScientificInstrument:
  type: MeasurementDevice
  performs_or_supports: Measurement

entity ScientificSample:
  type: ResearchMaterial
  subject_of:
    - Observation
    - Measurement
    - Experiment

# ---------- RELATIONSHIPS ----------
relationships:
  ScientificMetadata -> DESCRIBES -> ScientificResource
  ScientificResource -> BELONGS_TO_DOMAIN -> ScientificDomain
  Measurement -> MEASURES -> ScientificVariable
  Measurement -> PRODUCES -> MeasurementValue
  MeasurementValue -> EXPRESSED_IN -> Unit
  ScientificResource -> GENERATED_BY -> ScientificMethod
  Measurement -> MAY_USE -> ScientificInstrument
  ScientificInstrument -> MAY_HAVE -> Calibration
  Experiment -> MAY_USE -> ScientificSample
  Observation -> MAY_REFERENCE -> ScientificSample
  ScientificResource -> MAY_HAVE -> Uncertainty
  ScientificResource -> MAY_HAVE -> QualityMetadata
  ScientificResource -> MAY_HAVE -> TemporalMetadata
  ScientificResource -> MAY_HAVE -> SpatialMetadata
  ScientificMetadata -> MAY_REFERENCE -> Provenance

# ---------- MEASUREMENT ----------
rule measurement_context:
  Measurement SHOULD identify:
    - measured_variable
    - value
    - unit
    - method
  where applicable

rule unit_requirement:
  quantitative MeasurementValue SHOULD have Unit
  unless value is explicitly dimensionless

rule dimensional_consistency:
  comparisons and calculations MUST use dimensionally compatible quantities

rule valid_range:
  IF ScientificVariable has declared valid_range
  THEN MeasurementValue SHOULD be checked against that range

# ---------- UNCERTAINTY ----------
entity Uncertainty:
  type: ScientificQualityMetadata
  describes: QuantifiedOrQualifiedMeasurementUncertainty
  may_include:
    - uncertainty_value
    - uncertainty_type
    - confidence_level
    - coverage_factor
    - method

rule uncertainty_preservation:
  IF uncertainty is supplied
  THEN preserve uncertainty with MeasurementValue

rule derived_uncertainty:
  IF derived quantity is calculated
  THEN uncertainty SHOULD be propagated where method permits

rule uncertainty_not_error:
  Uncertainty != MeasurementError

# ---------- ACCURACY / PRECISION ----------
entity Accuracy:
  type: QualityCharacteristic
  describes: ClosenessToAcceptedOrTrueValue

entity Precision:
  type: QualityCharacteristic
  describes: RepeatabilityOrResolutionOfMeasurement

rule accuracy_precision:
  Accuracy != Precision

rule numeric_digits:
  number_of_decimal_places MUST NOT alone imply measurement accuracy

# ---------- INSTRUMENT ----------
rule instrument_identity:
  ScientificInstrument SHOULD have stable identifier where traceability is required

rule calibration:
  IF measurement validity depends on calibration
  THEN Calibration metadata SHOULD be preserved

rule calibration_period:
  IF calibration has validity period
  THEN measurement time SHOULD be evaluated against calibration validity

# ---------- METHOD ----------
rule method_traceability:
  ScientificMethod SHOULD be sufficiently described or referenced
  to support interpretation and reproducibility

rule method_version:
  IF ScientificMethod changes materially
  THEN method version SHOULD be preserved

rule method_dependency:
  results generated using different methods MUST NOT automatically be treated as directly equivalent

# ---------- EXPERIMENT ----------
entity Experiment:
  type: ScientificResource
  may_include:
    - hypothesis
    - protocol
    - independent_variable
    - dependent_variable
    - control
    - treatment
    - replicate
    - experimental_condition
    - result

rule experimental_context:
  Experiment SHOULD preserve conditions required to interpret results

rule replicate:
  replicate observations SHOULD remain individually traceable
  even when aggregate statistics are produced

# ---------- OBSERVATION ----------
entity Observation:
  type: ScientificResource
  records: ObservedState
  may_have:
    - observer
    - instrument
    - time
    - location
    - method
    - quality_flag

rule observation_context:
  temporal and spatial context SHOULD be preserved where scientifically relevant

# ---------- DATASET ----------
entity ScientificDataset:
  type: ScientificResource
  contains:
    - Observation
    - Measurement
    - DerivedData

rule dataset_schema:
  ScientificDataset SHOULD describe variables, units, dimensions, and encoding

rule derived_data:
  IF data is derived from source measurements
  THEN derivation method and provenance SHOULD be preserved

# ---------- PROVENANCE ----------
entity Provenance:
  type: MetadataEntity
  describes:
    - origin
    - authorship
    - processing_history
    - source_data
    - method_history
    - revision_history

rule provenance_chain:
  derived scientific resources SHOULD maintain traceable links to source resources

rule transformation_provenance:
  data transformations SHOULD record:
    - input
    - method
    - parameters
    - software_or_process
    - output
    - timestamp

# ---------- REPRODUCIBILITY ----------
rule reproducibility:
  ScientificMetadata SHOULD preserve sufficient context to enable:
    - interpretation
    - validation
    - replication where possible
    - computational reproduction where applicable

rule reproducibility_dependencies:
  MAY include:
    - method
    - instrument
    - software
    - parameters
    - sample
    - environmental_conditions
    - units
    - calibration
    - source_data

# ---------- VALIDATION ----------
validation:
  require:
    - identifiable_scientific_resource

  validate_if_present:
    - scientific_domain
    - variables
    - measurements
    - units
    - methods
    - instruments
    - uncertainty
    - calibration
    - temporal_context
    - spatial_context
    - provenance

  reject:
    - dimensionally_incompatible_quantity_comparison
    - measurement_without_interpretable_value_semantics
    - contradictory_unit_metadata
    - invalid_calibration_context where calibration is required
    - unsupported_accuracy_claim
    - broken_provenance_chain where traceability is required

# ---------- SEMANTIC EQUIVALENCE ----------
SEMANTIC_EQUIVALENCE:
  "scientific metadata" ~= "research metadata" WHEN describing scientific resources
  "measurement metadata" ~= "scientific measurement metadata"
  "experimental metadata" ~= "experiment metadata"
  "method metadata" ~= "methodological metadata"
  "instrument metadata" ~= "measurement instrument metadata"

  PRESERVE distinctions:
    measurement != observation
    accuracy != precision
    uncertainty != error
    sample != specimen_identifier
    method != instrument
    raw_data != derived_data
    result != interpretation
    scientific_metadata != scientific_data

# ---------- CONFIDENCE ----------
CONFIDENCE:
  explicit_scientific_metadata:
    score: 1.00

  explicit_measurement_with_unit:
    score: 1.00

  explicit_method_and_instrument:
    score: 1.00

  directly_derived_quantity:
    score: 0.95

  inferred_scientific_domain:
    score: 0.85

  inferred_method:
    score: 0.75

  incomplete_scientific_context:
    score: <= 0.60
    action: preserve uncertainty

# ---------- UPDATE MODEL ----------
UPDATE_MODEL:
  on_measurement_update:
    - preserve original measurement where provenance requires
    - validate value and unit
    - validate uncertainty
    - validate calibration context
    - update provenance

  on_method_update:
    - preserve previous method version
    - reassess affected results
    - update reproducibility metadata
    - record provenance

  on_instrument_update:
    - preserve instrument identity
    - update calibration metadata
    - reassess affected measurements
    - record provenance

  on_dataset_revision:
    - preserve previous dataset version
    - record changed variables or observations
    - revalidate schema
    - maintain provenance chain

# ---------- FLOW HASH ----------
FLOW_HASH:
  inputs:
    - ontology
    - scientific_entities
    - relationships
    - measurement_rules
    - uncertainty_rules
    - method_rules
    - provenance_rules
    - validation
    - semantic_equivalence
    - confidence
    - update_model

  canonicalisation:
    - normalise_whitespace
    - preserve_case_sensitive_identifiers
    - sort_unordered_property_sets
    - preserve_rule_order

  output:
    deterministic_semantic_hash

# ---------- AI CONSUMPTION ----------
AI_CONSUMPTION:
  interpret_as:
    - scientific metadata ontology
    - measurement reasoning model
    - research provenance model
    - reproducibility structure
    - scientific semantic retrieval model

  operations:
    - identify scientific resources
    - classify scientific domains
    - interpret variables and measurements
    - resolve units
    - evaluate uncertainty
    - distinguish accuracy and precision
    - resolve methods and instruments
    - validate calibration context
    - trace scientific provenance
    - support reproducibility assessment
    - preserve uncertainty

  constraints:
    - do_not_invent_measurements
    - do_not_invent_units
    - do_not_equate_accuracy_with precision
    - do_not_equate_uncertainty_with error
    - do_not_discard_raw_data provenance
    - do_not_assume methodological equivalence
    - preserve source and derivation context

# ---------- QUERY MODEL ----------
query:
  input:
    - scientific_resource
    - scientific_domain
    - variable
    - measurement
    - unit
    - method
    - instrument
    - sample
    - temporal_constraint
    - spatial_constraint

  resolve:
    1. identify ScientificResource
    2. resolve ScientificDomain
    3. resolve variables and measurements
    4. validate units and dimensions
    5. inspect uncertainty and quality
    6. resolve method and instrument
    7. apply temporal and spatial context
    8. trace provenance
    9. assess confidence

  output:
    - scientific_resource
    - variables
    - measurements
    - units
    - method
    - instrument
    - uncertainty
    - provenance
    - confidence

# ---------- INFERENCE ----------
inference:
  IF quantitative value and scientific variable exist
  AND unit semantics are compatible
  THEN infer Measurement candidate
  WITH confidence >= 0.90

  IF derived value has traceable source measurements and method
  THEN classify as DerivedData
  WITH confidence = 0.95

  IF instrument calibration is valid at measurement time
  THEN calibration_context = valid

  IF method, source data, parameters, and processing history are preserved
  THEN reproducibility_support = high

  IF critical scientific context is missing
  THEN reduce confidence
  AND preserve unresolved metadata requirements

# ---------- OUTPUT CONTRACT ----------
output:
  ontology_entity: ScientificMetadata
  scientific_context: machine_interpretable
  measurements: unit_aware
  uncertainty: preserved
  methods: traceable
  instruments: identifiable
  provenance: preserved
  reproducibility_context: explicit
  uncertainty_state: explicit
  semantic_retrieval: enabled

end flow